The output \(\mathrm {\tilde{M}}_i\)  should be further turned into an orthographic projection matrix, i.e. \(\mathrm {\hat{M}}_{i}\mathrm {\hat{M}}_{i}^{\mathrm {T}}=\mathrm {I}_{2}\) . The orthogonalization can be done in several ways, like Gram-Schmidt procedure [1]}, projections onto \(\mathrm {SO}(3)\)  [2]}, [3]}. We follow [4]}, [5]} to use Singular Value Decomposition (SVD) as the orthogonalization method, as it is shown that SVD produces better rotation estimation than Gram-Schmidt in many supervised and unsupervised tasks [6]}. The orthogonalization process can be expressed as:
\(\mathrm {\hat{M}}_{i}=\mathrm {UV^T} \quad \mathrm {s.t.} \quad \mathrm {\tilde{M}}_i=\mathrm {U\Sigma V^T},\) 
